Loss modelling framework.
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Updated
Jun 24, 2026 - Python
Loss modelling framework.
Repository of GEMAct source code. Enjoy!
Functions from the book "Reinsurance: Actuarial and Statistical Aspects"
Destruction rate modeling with the Maxwell Boltzmann Bose Einstein Fermi Dirac (MBBEFD) distribution
An actuarial stochastic modeling library in python.
AI-powered Reinsurance Contracts & Parties management
SCOR Datathon in 2020. Acquired and processed open data, predicted level of Glycohemoglobin, Cholesterol and probability of diabetes, then identified the probability change with Random Survival Forest to suggest improvements to a user.
File validation and data sampling toolkit for the Simplitium Open Exposure Data (OED) (re)insurance exposure data format
Prediction of market premiums for property damage and business interruption insurance products. Added natural hazard data and stacked 3 best models as the final model.
You will be able to execute transactions against a real Hyperledger Fabric blockchain network for Reinsurance Claims that interacts with a blockchain network.
AI-powered document review for reinsurance workflows.
A collection of reinsurance contracts retrieved from SEC filings, with additional metadata generated by LLMs.
AI-powered daily job discovery for insurance operations. Claude API scoring, automated CV generation, email alerts. 30+ job sources across Europe.
Location-level Florida hurricane catastrophe model in Python: HURDAT2-calibrated hazard + physical wind field (Holland, Vickery-Wadhera, Kaplan-DeMaria), synthetic exposure, HAZUS-anchored vulnerability, OED/YLT/ELT formats, XoL reinsurance with reinstatements, gross/net AEP/OEP curves & PMLs, historical backtesting (Andrew, Ian).
A Dask library for Big Data processing in Python demo
Severity modelling for insurance pricing - spliced distributions, DRN, composite regression, EQRN extreme quantiles
This Module tries to combine and simplify all aspects of Reinsurance Invoicing, Analysis, Slip Generation, Quotation & Data science. Later to be Intergrated into a cross platform API.
End-to-end reinsurance analytics: EVT, treaty structuring, and program optimization using freMTPL2 data
Reinsurance exercise: pricing and ceded-claims analysis with Jupyter notebooks.
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